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.env

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+GOOGLE_API_KEY = "AIzaSyCXtDpJFJVvI_FDO_X4oOXuQnFnL5xEYoM"

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+load_dotenv()
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+            
+    return text
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+    
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context,
+    make sure to provide all the details, if the answer is not in the provided context just say, 
+    "answer is not available in the context", don't provide the wrong answer.\n\n
+    Context: \n {context}?\n
+    Question: \n {question}\n
+    
+    Answer:
+    """
+    
+    model = ChatGoogleGenerativeAI(model = "gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+    
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")  
+    
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+    
+    chain = get_conversational_chain()
+    
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs = True)
+    
+    st.write("Reply: ", response["output_text"])
+        
+def main():
+    st.set_page_config("Chat With Multiple PDF")
+    st.header("Chat with Mulitple PDF using Gemini 👨‍💻") 
+    
+    user_question = st.text_input("Ask a Question from the PDF Files")
+    
+    if user_question:
+        user_input(user_question)
+    
+    with st.sidebar:
+        st.title("Menu: ")
+        pdf_docs = st.file_uploader("Upload your PDF Files and click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing...."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+            
+    
+if __name__ == "__main__":
+    main()